25 November 2015

Campylobacteriosis

  • Campylobacter 'twisted bacteria'

  • Predominantly food-borne.

  • NZ's most notified disease.

  • 6770 cases in 2014.

  • For every case notified, there are an estimated 7.6 community cases. (Wheeler et. al. 1999)

  • Each case is estimated to cost around $600. (Lake et. al. 2009)

Manawatu sentinel surveillance site

Manawatu campylobacteriosis cases

New Zealand campylobacteriosis cases

MLST for Campylobacter

  • Seven housekeeping genes (loci).

  • Unique genes are assigned different numbers (alleles).

  • The combination of alleles gives the multilocus sequence type.

MLST for Campylobacter

ST aspA glnA gltA glyA pgm txt uncA
474 2 4 1 2 2 1 5
61 1 4 2 2 6 3 17
2381 175 251 216 282 359 293 102
48 2 4 1 2 7 1 5
2370 1 4 2 2 6 5 17

MLST distribution of human cases

MLSTs are source specific

MLSTs differ by rurality

MLSTs differ by rurality

Questions

  • Does the proportion of human cases attributed to each source change seasonally?

  • Did the intervention in the poultry industry work?

  • Is attribution related to rurality?

Modelling

R package islandR

islandR model

\[ P(\mathsf{st}) = \sum_j P(\mathsf{st} \mid \mathsf{source}_j) P(\mathsf{source}_j) \]

islandR model

\[ P(\mathsf{st}) = \sum_j \underbrace{P(\mathsf{st} \mid \mathsf{source}_j)}_\text{genomic model} P(\mathsf{source}_j) \]

islandR model

\[ P(\mathsf{st}) = \sum_j \underbrace{P(\mathsf{st} \mid \mathsf{source}_j)}_\text{genomic model} \underbrace{P(\mathsf{source}_j)}_\text{attribution to source} \]

Genomic model

D. Wilson (2009)

Assume that Campylobacter genotypes arise from two or more homogeneous mixing populations where we have

  • Mutation, where novel alleles are produced.

  • Recombination, where the allele at a given locus has been observed before, but not in this allelic profile.

  • Migration between sources, where genotypes have been observed previously.

Mutation

ST aspA glnA gltA glyA pgm txt uncA
474 2 4 1 2 2 1 5
? 2 4 1 2 29 1 5


  • We have a novel allele at the pgm locus.

  • We assume this genotype has arisen through mutation.

Recombination

ST aspA glnA gltA glyA pgm txt uncA
474 2 4 1 2 2 1 5
? 2 4 1 2 1 1 5
45 4 7 10 4 1 7 1
3718 2 4 1 4 1 1 5


  • We have seen this pgm allele before, but haven't seen this genotype.

  • We assume it arose through recombination.

Migration

ST aspA glnA gltA glyA pgm txt uncA
474 2 4 1 2 2 1 5
? 2 4 1 2 2 1 5


  • This is just 474. We've seen it before, but possibly not on this source.

  • We assume it arose through migration.

Genomic model

\[ \phi(y \mid k,X) = \sum_{c\in X} \frac{M_{S_ck}}{N_{S_c}} \prod_{l=1}^7 \left\{\begin{array}{ll} \mu_k & \text{if $y^{l}$ is novel,}\\ (1-\mu_k)R_k\sum_{j=1}^K M_{jk}f^l_{y^lj} & \text{if $y^{l}\neq c^l$}\\ (1-\mu_k)\left[1 - R_k(1 - \sum_{j=1}^K M_{jk}f^l_{y^lj})\right] & \text{if $y^{l}=c^l$} \end{array} \right. \]

  • \(X\) are the previously observed sequences.
  • \(c\) comes from source \(S_c\).
  • \(\mu_k\) be the probability of a novel mutant allele from source \(k\).
  • \(R_k\) be the probability that a type has undergone recombination in souce \(k\).
  • \(M_{jk}\) be the probability of an allele migrating from source \(j\) to \(k\).
  • \(f^l_{aj}\) be the frequency with which allele \(a\) has been observed at locus \(l\) in those genotypes sampled from source \(j\).

islandR model

\[ P(\mathsf{st}) = \sum_j \underbrace{P(\mathsf{st} \mid \mathsf{source}_j)}_\text{genomic model} \underbrace{P(\mathsf{source}_j)}_\text{attribution to source} \]

islandR model

\[ P(\mathsf{st} \mid \underbrace{t, \mathbf{x}}_\text{covariates}) = \sum_j \underbrace{P(\mathsf{st} \mid \mathsf{source}_j)}_\text{genomic model} \underbrace{P(\mathsf{source}_j \mid t, \mathbf{x})}_\text{attribution with covariates} \]

islandR attribution

Nested within each source \(j\) we have \[ \begin{aligned} \mathsf{logit}\left(P(\mathsf{source}_j \mid t, \mathbf{x})\right) &= \mathsf{Location}_\mathbf{x} \cdot \mathbf{1}\left[t \geq 2008\right] \cdot \mathsf{Month}_t + \epsilon_{\mathbf{x}t}\\ \epsilon_{\mathbf{x}t} &\sim \mathsf{Normal}(\rho \epsilon_{\mathbf{x}(t-1)}, \sigma^2) \end{aligned} \]

  • Covariates are estimated as a Gibbs step conditional on correlation \(\rho\), variance \(\sigma^2\) and \(P(\mathsf{source}_j \mid t, \mathbf{x})\).

  • \(\phi\) and \(\sigma^2\) are then updated using Gibbs conditional on the covariates and \(P(\mathsf{source}_j \mid t, \mathbf{x})\).

  • \(P(\mathsf{source}_j \mid t, \mathbf{x})\) are block updated from the full conditional, interleaved with Metropolis Hastings steps.

Results

Urban attribution

Rural attribution

Urban attribution

Rural attribution

Summary

  • Urban cases tend to be more associated with poultry, and rural cases with ruminants.

  • There does seem to be some evidence for seasonality in attribution.

  • The poultry intervention in 2007 resulted in a marked reduction in poultry related cases in urban areas, less strong in rural areas.

  • Very few cases associated with water or other sources.

  • Limitation: Genomic model assumed constant through time.

Acknowledgements

  • MidCentral Public Health Services: Tui Shadbolt,
    Adie Transom
  • Medlab Central: Lynn Rogers
  • ESR: Phil Carter
  • mEpiLab: Rukhshana Akhter, Julie Collins-Emerson,
    Ahmed Fayaz, Anne Midwinter, Sarah Moore, Antoine Nohra, Angie Reynolds
  • Ministry for Primary Industries: Donald Campbell,
    Peter van der Logt
  • Petra Müllner
  • Nigel French

Urban water/other attribution

Rural water/other attribution

Urban water/other attribution

Rural water/other attribution